AI-900 Describe features of generative AI workloads on Azure Practice Test 3 — 15 Questions

Question 1

A social media platform uses Azure OpenAI Service to generate summaries of user comments. The development team discovers that sometimes the generated summaries include offensive or harmful language that was present in the original comments. The team wants to ensure that the generated output is always free of hate speech, profanity, and self-harm references. What should the team configure in the Azure OpenAI Service?

Accepted Answer

Configure a content filter. Option B is correct because Azure OpenAI Service provides built-in content filtering that can be configured to block hate speech, profanity, and self-harm references in both input prompts and generated completions. This ensures that even if offensive language appears in the original user comments, the generated summaries will be free of such harmful content. The content filter operates at the service level, applying predefined severity thresholds to filter out undesirable language.

Answer

Set the temperature parameter to 0

Answer

Increase the max_tokens parameter

Answer

Use a grounding source

Question 2

A marketing team wants to create original images for advertisements based on text descriptions. Which Azure OpenAI Service model capability should they use?

Accepted Answer

DALL-E. DALL-E is the correct choice because it is the Azure OpenAI Service model specifically designed for generating original images from natural language text descriptions. Unlike other models in the suite, DALL-E uses a diffusion-based architecture to create photorealistic or stylized visuals based on prompt inputs, making it ideal for the marketing team's goal of producing custom advertisement imagery.

Answer

GPT-3.5

Answer

Codex

Answer

Azure Speech-to-Text

Question 3

A developer uses Azure OpenAI Service to generate long-form articles. The developer notices that the model tends to repeat the same sentence structures and vocabulary, making the output monotonous. Which parameter should the developer increase to reduce this repetition?

Accepted Answer

C. Increasing the 'frequency penalty' parameter (option C) reduces repetition by penalizing tokens that have already appeared in the generated text. This encourages the model to use a wider variety of sentence structures and vocabulary, making the output less monotonous.

Answer

A

Answer

B

Answer

D

Question 4

A developer is using Azure OpenAI Service to classify customer support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. The developer provides three labeled examples for each category in the prompt to improve the model's accuracy. What technique is the developer applying?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer is providing a small number of labeled examples (three per category) directly in the prompt to guide the model's output without updating the model's weights. This approach leverages the model's in-context learning ability, where the examples act as a pattern for the model to follow when classifying new tickets.

Answer

Fine-tuning

Answer

Prompt engineering

Answer

Retrieval-augmented generation

Question 5

A meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?

Accepted Answer

Whisper. Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.

Answer

GPT-4

Answer

DALL-E 2

Answer

Codex

Question 6

A company uses Azure OpenAI Service to generate product descriptions for an e-commerce site. They want to ensure that the generated descriptions never contain offensive, violent, or hateful content. Which built-in feature should the developer enable in the Azure OpenAI Service?

Accepted Answer

Content Filtering. Content Filtering is a built-in safety feature in Azure OpenAI Service that automatically detects and blocks harmful content categories such as hate, violence, sexual, and self-harm. It operates at the input prompt and output completion level, ensuring generated product descriptions remain compliant with content policies without requiring custom development.

Answer

Prompt Engineering

Answer

Fine-tuning

Answer

Token Limit

Question 7

A company uses Azure OpenAI Service to generate creative product descriptions. They want to increase the randomness and variety of the generated outputs to produce more diverse suggestions. Which parameter should they increase?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.

Answer

Top_p

Answer

Frequency penalty

Answer

Presence penalty

Question 8

A company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?

Accepted Answer

Use the 'Add your data' feature (also known as 'Azure OpenAI on your data'). The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.

Answer

Increase the temperature parameter

Answer

Increase the frequency_penalty parameter

Answer

Use the system message to instruct the model to only use provided content

Question 9

A developer uses Azure OpenAI Service to generate product descriptions. They want to ensure that the model only considers the most likely tokens that together have a cumulative probability of 0.95, ignoring very low-probability tokens that could lead to nonsensical outputs. Which parameter should they configure?

Accepted Answer

Top_p. Option B (Top_p) is correct because the developer wants to limit token selection to those with a cumulative probability of 0.95, which is exactly what the Top_p (nucleus sampling) parameter controls. By setting Top_p to 0.95, the model will only consider the smallest set of tokens whose combined probability mass reaches 0.95, effectively ignoring low-probability tokens that could produce nonsensical outputs.

Answer

Temperature

Answer

Frequency penalty

Answer

Presence penalty

Question 10

A developer uses Azure OpenAI Service to generate product name suggestions. They want to ensure the model never outputs a specific word, such as 'Corporation', because it is too formal for their brand. Which parameter should the developer configure to reduce the probability of that token being generated?

Accepted Answer

Logit Bias. Logit Bias is the correct parameter because it directly modifies the logits (raw prediction scores) for specific tokens before the softmax function, allowing the developer to reduce the probability of generating a particular token like 'Corporation'. By setting a negative bias value for that token's ID, the model is less likely to output it, even if it would otherwise be a high-probability choice. This is the only parameter that provides token-level control over output content.

Answer

Temperature

Answer

Top P (Nucleus Sampling)

Answer

Frequency Penalty

Question 11

A marketing team uses Azure OpenAI Service to generate taglines for a new advertising campaign. They want the output to be more predictable and less surprising, sticking to the most common phrases and avoiding unusual combinations. Which parameter should they decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic, favoring high-probability tokens and common phrases, which reduces surprise and unusual combinations. This directly aligns with the team's goal of predictable, conservative taglines.

Answer

Top P

Answer

Frequency penalty

Answer

Presence penalty

Question 12

A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?

Accepted Answer

Use Azure OpenAI On Your Data to connect to a product database so the model retrieves and references only the provided specifications.. Option A is correct because Azure OpenAI On Your Data allows the developer to ground the model's responses in a specific data source, such as a product database. This ensures the model retrieves and references only the provided specifications, preventing the generation of external or invented information (hallucinations). By using this feature, the model's outputs are strictly based on the connected data, aligning with the requirement for factual accuracy.

Answer

Increase the frequency penalty to discourage the model from repeating common phrases.

Answer

Decrease the temperature to 0 so the model always picks the most likely next token, making it more predictable.

Answer

Enable content filtering to block any outputs that contain harmful or biased language.

Question 13

A developer uses Azure OpenAI Service to generate data transformation scripts. The generated scripts sometimes contain logical errors. To make the model's output more deterministic and reduce variability, which parameter should the developer decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering temperature (e.g., from 0.7 to 0.1) makes the model more deterministic and focused, reducing variability and the likelihood of logical errors in generated scripts. This is the correct parameter to adjust for more consistent, less creative responses.

Answer

Top_p

Answer

Frequency penalty

Answer

Presence penalty

Question 14

A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains repetitive phrases or gets stuck in loops. They want to reduce this behavior without changing the overall creativity of the model. Which parameter should they adjust?

Accepted Answer

Increase the frequency_penalty parameter.. Increasing the frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. This directly addresses the repetitive loops without altering the overall creativity, as frequency_penalty specifically targets token frequency rather than randomness or diversity.

Answer

Decrease the temperature parameter.

Answer

Increase the presence_penalty parameter.

Answer

Decrease the top_p parameter.

Question 15

A marketing team uses Azure OpenAI Service to generate ad copy. They notice the model sometimes uses offensive language. Which Azure OpenAI feature should they use to automatically block such content?

Accepted Answer

Enabling content filtering. Option C is correct because Azure OpenAI Service includes built-in content filtering that automatically detects and blocks offensive or harmful language in both prompts and completions. This feature uses AI-based classifiers to enforce responsible AI policies without requiring manual configuration of model parameters.

Answer

Setting the temperature parameter to 0.0

Answer

Using the frequency_penalty parameter

Answer

Configuring the max_tokens parameter